Data mining and analysis of lift maintenance records

Abstract

Today, with the rapid development of science and technology, the lift, as an important means of transportation in high-rise buildings, plays a vital role in people’s daily life. The resulting safety issues are also getting more and more attention. In this project, the lift failure data provided by the lift company was analyzed and found that the failure frequency of the lift is linked to the fre quency of people’s daily activities, especially on weekdays and rush hours, and will be accompanied by a certain periodicity. Therefore, to predict the specific location and fault category of the lift failure on a certain day, this dissertation uses the Autoregressive Integrated Moving Average Model(AMIRA) method to predict the lift failure rate after the seasonal adjustment of the fault data.Combined with the chi-squared automatic interaction detector (CHAID) decision tree algorithm, the lift fault position and the lift fault type are successfully predicted. Finally, the fuzzy comprehensive evaluation method is used to assess the risk of a single unit of the lift under test, to realize the monitoring of the whole lift system.